Adam Optimizer vs Gradient Descent - Stack Overflow
Aug 25, 2018 · AdamOptimizer is using the Adam Optimizer to update the learning rate. Its is an adaptive method compared to the gradient descent which maintains a single learning rate for all …
How can Stochastic Gradient Descent (SGD) avoid the problem of local ...
Jul 26, 2024 · The path of stochastic gradient descent wanders over more places, and thus is more likely to "jump out" of a local minimum, and find a global minimum (Note*). However, stochastic gradient …
Numeric Gradient Descent in python - Stack Overflow
Dec 9, 2020 · The gradient descent takes us further from the minimal at each step because the update value is too large: Note that the gradient descent we gave as an example makes only 5 steps while …
How to set adaptive learning rate for GradientDescentOptimizer?
Nov 25, 2015 · 88 Gradient descent algorithm uses the constant learning rate which you can provide in during the initialization. You can pass various learning rates in a way showed by Mrry. But instead of …
gradient descent using python and numpy - Stack Overflow
Jul 22, 2013 · Below you can find my implementation of gradient descent for linear regression problem. At first, you calculate gradient like X.T * (X * w - y) / N and update your current theta with this …
python - Is SGD optimizer in PyTorch actually does Gradient Descent ...
Jun 4, 2022 · My understanding about the optimizer here is that the SGD optimizer actually does the Mini-batch Gradient Descent algorithm because we feed the optimizer one batch of data at one time. …
optimization - What is the difference between gradient descent and ...
Mar 23, 2014 · Gradient descent is an iterative operation that creates the shape of your function (like a surface) and moves the positions of all input variables until the model converges on the optimum …
machine learning - why gradient descent when we can solve linear ...
Aug 12, 2013 · what is the benefit of using Gradient Descent in the linear regression space? looks like the we can solve the problem (finding theta0-n that minimum the cost func) with analytical method so …
algorithm - R: Performing Gradient Descent - Stack Overflow
Jan 19, 2022 · I am trying to learn more about optimization algorithms, and as a learning exercise - I would like to try an optimize a mathematical function using the (famous) gradient descent algorithm …
Why go through the trouble of expectation maximization and not use ...
Oct 14, 2023 · Why can we not directly optimize the LHS using gradient descent? Why go through the entire create the evidence lower bound and then optimize using the EM algorithm?